Hepatitis

Leveraging viral genome sequences and machine learning models for identification of potentially selective antiviral agents.

Viral genome sequencing provides valuable information for antiviral development, yet its integration with machine learning for virtual screening remains underexplored. To bridge this gap, viral genome sequences were combined with structural data of approved and investigational antivirals to identify virus-selective agents. In parallel, quantitative structure-activity relationship (QSAR) models wer...

Cancer vaccines and the future of immunotherapy.

Vaccines have had a major impact on the control of infectious disease, most recently by helping to combat the COVID-19 pandemic. Prophylactic cancer vaccines have prevented several malignancies by protecting against cancer-causing pathogens. By contrast, therapeutic vaccines training the immune system to eliminate established tumours are now showing real promise in clinical settings. In the adjuva...

Demarcating the Development of Cirrhosis and Its Complications via Plasma Proteomic Features in an Asymptomatic Population.

The early detection of liver cirrhosis and its complications is a conundrum in clinical practice. We aim to address this conundrum using proteomic fea...

Improving Hepatitis B outcome prediction with ensemble machine learning: A study on predictive models and interpretability.

OBJECTIVE: Hepatitis B virus (HBV) is a significant global health threat, responsible for severe liver diseases such as liver failure, cirrhosis, and ...

Signature gene expression model for quantitative evaluation of MASH-like liver injury in mice.

Metabolic dysfunction-associated steatotic liver disease (MASLD) is a spectrum of chronic pathologic conditions strongly associated with metabolic syn...

Stem loop binding protein promotes SARS-CoV-2 replication via -1 programmed ribosomal frameshifting.

The -1 programmed ribosomal frameshifting (-1 PRF) in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is crucial for keeping the balance ...

Integrative exome sequencing and machine learning identify MICB and interferon pathway genes as contributors to SSc risk.

OBJECTIVES: Systemic sclerosis (SSc) is a complex autoimmune disease with both known and unidentified genetic contributors. While genome-wide associat...

Predictive Stability in Biopharmaceuticals and Vaccines: Perspectives and Recommendations Towards Accelerating Patient Access.

The perspective review discusses predictive stability computational modeling and scientific risk-based approaches to prospectively assess long-term st...

28-day all-cause mortality in patients with alcoholic cirrhosis: a machine learning prediction model based on the MIMIC-IV.

To develop and validate a machine learning prediction model for 28-day all-cause mortality in patients with alcoholic cirrhosis using data from the MI...

Challenges and Opportunities for Global Cervical Cancer Elimination: How Can We Build a Model for Other Cancers?

Cervical cancer remains a leading cause of cancer-related death among women globally, despite the availability of effective prevention through human p...

Bayesian optimization and machine learning for vaccine formulation development.

Developing vaccines with a better stability is an area of improvement to meet the global health needs of preventing infectious diseases. With the adva...

AVP-HNCL: Innovative Contrastive Learning with a Queue-Based Negative Sampling Strategy for Dual-Phase Antiviral Peptide Prediction.

Viral infections have long been a core focus in the field of public health. Antiviral peptides (AVPs), due to their unique mechanisms of action and si...

Hepatitis B In Silico Trials Capture Functional Cure, Indicate Mechanistic Pathways, and Suggest Prognostic Biomarker Signatures.

In silico trials, utilizing mathematical models calibrated with clinical data, present a transformative approach to expedite drug development. We prop...

Factors Influencing the Binding of HIV-1 Protease Inhibitors: Insights from Machine Learning Models.

HIV-1 protease inhibitors are crucial for antiviral therapies targeting acquired immunodeficiency syndrome (AIDS). Hundreds of HIV-1 protease complexe...

Large Scale MRI Collection and Segmentation of Cirrhotic Liver.

Liver cirrhosis represents the end stage of chronic liver disease, characterized by extensive fibrosis and nodular regeneration that significantly inc...

Biophysical modeling for accurate T cell specificity prediction of viral and tumor antigens.

Accurate predictions of T cell receptor (TCR) specificity remain an important open problem in immunology, with broad implications for vaccine design, ...

High Throughput Meta-analysis of Antimicrobial Peptides for Characterizing Class Specific Therapeutic Candidates: An In Silico Approach.

The increasing incidence of antimicrobial resistance is becoming a serious concern worldwide and requires newer drugs. Recent evidence has shown growi...

Discovering the metabolic pathway of liver disease by breath mass spectrometry combined with machine learning.

On account of the low concentration and complex background, most methods for detecting VOCs in exhaled breath samples required preconcentration prior ...

Deep learning architectures for influenza dynamics and treatment optimization: a comprehensive review.

As a major worldwide health concern, influenza still requires precise modeling of flu dynamics and efficient treatment approaches. Deep learning archi...

Enhancing liver disease diagnosis with hybrid SMOTE-ENN balanced machine learning models-an empirical analysis of Indian patient liver disease datasets.

INTRODUCTION: The liver is one of the vital organs of human body that performs some of the most crucial biological processes such as protein and bioch...